Mental health advice on TikTok

Abstract

In this paper, we provide the first, large-scale corpus-pragmatic analysis of mental health advice by social media influencers on TikTok. We identify advice-giving in large datasets focusing on if-conditionals as a specific form that allows us to analyse how the audience is positioned relative to a need and the solution which is then proposed. To identify the different ways in which mental health issues are presented, we use an adapted version of the ‘mental health quotient’ (Newson and Thiagarajan, 2020), as a linguistically informed framework for differentiating between lay discussions of mental health and those that invoke specific disorders. We sample a corpus of over 27,000 TikTok videos from 85 mental health influencers, using corpus-scale identification to extract and analyse if-conditionals produced by mental health professionals and wellness influencers. Our analysis of the protasis shows how these two types of influencers use prompts that share some similarities but also rely on fundamentally different models of healthcare. The relationship between these prompts and the information and recommendations in the apodosis show how health professionals rely on diagnostic information and therapeutic advice, while wellness influencers recommend embodied practice and products to treat mental health issues. These findings set out the distinctive ecosystem of healthcare which is emerging within the algorithmically driven contexts of sites like TikTok.